censtatd_table_infoGet metadata for a Hong Kong C&SD statistical table without pulling the data: title, footnotes (tablenote), and source/contact. Use to confirm a table id and read measurement caveats before calling censtatd_get_table. Returns title, notes and source for the given id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Table id, e.g. '310-31001'. | |
| lang | No | Language. Default en. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description confirms the tool is read-only ('without pulling the data') and non-destructive, aligning with all annotations (readOnlyHint, destructiveHint, idempotentHint). It adds specific behavioral details (returns title, notes, source) beyond the annotations, providing clarity on what the tool does without overpromising.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no redundancy. The first sentence packs core purpose, resource, and return values. The second provides a clear usage directive. Every word serves a purpose, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (2 params, no output schema), and the description covers all necessary aspects: purpose, return items, usage in a workflow, and implicit safety via annotations. It is fully complete for an agent to decide when and how to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter descriptions in the schema are clear. The tool description does not add extra semantic depth beyond what the schema provides (e.g., it doesn't explain the format of 'id' or the values of 'lang'). Baseline score of 3 is appropriate as the description doesn't fall short but also doesn't elevate understanding further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'metadata for a Hong Kong C&SD statistical table', and explicitly lists what is returned (title, footnote, source/contact). It distinguishes itself from the sibling tool 'censtatd_get_table' by saying 'without pulling the data', making the tool's specific role unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this tool to 'confirm a table id and read measurement caveats before calling censtatd_get_table', giving clear context and a sequential dependency. It implies not to use it when full data is needed. A slight improvement would be an explicit 'do not use' condition, but the guidance is already strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target a distinct action or resource, and the long routing descriptions make choices like ask_pipeworx vs ask_pipeworx_grounded vs deep_research clear. The main weak spots are ask_pipeworx_beta being currently identical to ask_pipeworx and the six overlapping Polymarket tools, but each has a discernible workflow.
Naming has internally consistent subfamilies such as censtatd_*, ask_pipeworx*, and polymarket_*, but overall it mixes verb-first names (get_table, validate_claim, subscribe), noun-first names (entity_profile, bet_research, pipeworx_feedback), and bare imperatives (remember, forget, recall). The set is readable but does not follow one convention.
35 tools exceeds the 25+ threshold and feels heavy, especially since many tools (generate_llms_txt, scan_dependency, pipeworx_feedback, pipeworx_trending) are unrelated to the HK Census core implied by the server name. The broad Pipeworx scope explains the width, but the surface is still large for an agent to navigate efficiently.
For a read-only research/data-access gateway, coverage is strong: generic lookup, grounded verification, deep research, entity profile/compare/change, entity resolution, memory, and subscription lifecycle are all represented. Minor gaps exist, such as no subscription update, no explicit bulk/export path, and fewer HK C&SD convenience wrappers, but agents can work around them.